Image deblurring

Recovering a sharp version of an input blurred image is challenging in computational photography and digital image processing. Recent progresses have been made in algorithms to address the ill-posedness of the problem. Yet, the results are imperfect. This thesis presents two approaches that explore latent priors from observations to provide a better condition for image deblurring. The first approach is to use an additional correlated image. The input is a pair of images, one is blurry and the other is noisy. By combining the information from two degraded images, we are able to estimate a very accurate blur kernel and to restore a high-quality original image, which is not possible from single image deblurring or single image denoising. In addition, we present a more general framework of image deblurring with multiple correlative images, which go beyond the blurred/noisy image pair for a sequence of blurred images. Our approach is based on the assumptions that all blurred images caused by different blurs are derived from the same original image and that different blurs result in the loss of different frequency components during imaging. By fusing information from complementary frequency components, ambiguous solutions in both kernel estimation and image restoration can be further eliminated. Another prominent problem in image deblurring with multiple images is how to accurately align image pairs. Using the sparseness prior of blur kernels, a novel method is proposed to align multiple images in a fully automatic manner. Thus the method of image deblurring becomes effective and practical for the pictures captured under dim light conditions by a hand-held camera. The second approach is to choose the prior of sharp image structure. In image deblurring with blurred/noisy image pair, the noisy image acted as the guide image to provide large-scale and sharp edges to accurately estimate a blur kernel and restore a high-quality image. This insight can be further applied to single image deblurring. As we observed, the reconstructed image usually contains unpleasant ringing artifacts, due to the ill-posedness of image deconvolution even if the blur kernel was the ground truth. To suppress ringing artifacts and preserve recovered signals, we require the guide image to distinguish between texture regions and flat regions. Thus, we develop an inter-scale and intra-scale deconvolution framework to progressively recover such a guide image, which is then used to adaptively suppress ringing artifacts in texture regions and flat regions. Our progressive deconvolution approach can produce very promising results not only in synthetic experiments, but also in various real scenarii. The intensive experimental results demonstrate that our approach outperforms the state-of-the-art techniques and have wide applications in scientific and digital entertainment fields.